This paper presents novel applications of the Bayesian inversion approach and the simulated annealing method (SAM) for identifying and reducing uncertainties involved in automatic history matching and forecasting processes which are of paramount importance for optimisation of hydrocarbon recovery. The Bayesian inversion approach enables the priori knowledge about the inversion parameters to be incorporated into the objective function to form a posteriori, in conjunction with the likelihood function reflecting the mismatch between the history data and the data predicted from the numerical model. The simulated annealing method (SAM) bas been applied to escape local optima which may still exist in the posteriori objective function, in order to further reduce the nonuniqueness of the inversion solutions.


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